Hhow Many Times Repeating an Action Does It Take to Form a Habitpeer Review

Introduction

Sometimes people find themselves mindlessly watching Telly while they had the intention to exist more physically agile; eating sweets while they wanted to consume more healthily; or lashing out at others while they wanted to be more patient or open-minded. Sounds familiar? Although people may oft be able to control themselves in social club to achieve long-term goals such as healthy living or maintaining satisfactory relationships, there are also many instances in which they are unable or unwilling to exert self-control (e.g., when temptations are strong or when tired; eastward.m., Muraven and Slessareva, 2003; Baumeister et al., 2007; Hofmann et al., 2010). Also, some people are less successful in decision-making their behaviors than others (Schmeichel and Zell, 2007). In these cases, people oft revert to effortless, habitual beliefs (Ouellette and Wood, 1998; Webb and Sheeran, 2006; Neal et al., 2013) – often bad habits. This reliance on habits may, even so, too be used to peoples' advantage if they manage to form expert habits that are in line with their long-term goals. Indeed, recent research suggests that people who are successful in controlling their beliefs, more effortlessly rely on good habits (Adriaanse et al., 2014; Gillebaart and de Ridder, 2015). Just how are practiced habits formed?

Research on addiction formation has shown that beliefs is probable to become habitual when it is frequently and consistently performed in the same context (e.k., Ouellette and Forest, 1998). For example, when i frequently and consistently eats vegetables for lunch, at some signal eating vegetables for lunch volition become a habit. This is considering the frequent co-occurrence of context and behavior instigates an association that may guide future behavior (e.k., Aarts and Dijksterhuis, 2000; Neal et al., 2012). Specifically, when encountering a context (e.grand., having lunch) that is associated with a sure behavior (e.g., eating vegetables), this context volition automatically trigger this associated behavior. Hence, once a practiced habit is formed, it is rather effortless to perform desired behavior. Even so, the procedure of addiction formation itself may vary in the amount of try needed – although some people manage to form certain habits as speedily as 18 days, others need every bit much as one-half a year (Lally et al., 2010). This raises the question how exactly do habits form over time?

Although enquiry on habit formation is withal in its infancy, contempo studies have uncovered some of the mechanisms that underlie the habit germination process. Ane of the main findings is that the addiction formation process within individuals unfolds asymptotically (Lally et al., 2010; Fournier et al., 2017). That is, addiction strength increases steeply at first, and then levels off. In addition, studies that studied habit formation on the group level (i.e., averaging over participants) have provided insight into the processes that facilitate such increases in habit forcefulness. Specifically, the frequency and consistency with which the desired behavior is performed, the inherently rewarding nature of the behavior, a comfortable environment (e.k., no threats or obstacles), and easy rather than difficult behaviors have been shown to facilitate the procedure of habit germination (Kaushal and Rhodes, 2015; Fournier et al., 2017).

As well these factors, there are yet many others unexplored that may explain the variation in the time it takes people to form a habit. I such probable candidate is self-control capacity. That is, habit germination crucially depends on the repeated performance of behavior that is in line with i's long-term goal. The initiation of such new behavior, equally well every bit the inhibition of acting upon short-term temptations is likely to require effortful self-control, particularly in the early stages of habit formation. Indeed a written report among teenagers indicates that those who initially had higher cocky-control chapters reported having stronger meditation habits after three months of meditation sessions (Galla and Duckworth, 2015, Report 5). Other studies revealed that habit strength mediates the effect of self-command forcefulness and beliefs. Specifically, self-command was related to increased habit strength, which was in turn related to increased exercise beliefs (Gillebaart and Adriaanse, 2017) and decreased snack intake (Adriaanse et al., 2014). However, although these studies have indicated that self-control is related to habit strength, they do not provide insight in the role of cocky-control capacity in the initial stages of addiction germination.

The current study was a first try to rails how self-command capacity affects the development of skillful habits in daily life over a relatively long period of fourth dimension. We expected both repeated goal-coinciding behavior operation and self-command capacity to facilitate the formation of good habits. Possibly, cocky-control capacity may touch habit formation via increased behavior operation (as the initiation of new behavior and inhibition of conflicting beliefs requires self-control at first). To test these hypotheses, we recruited people who wanted to grade a good habit in the domain of health behavior (eating fruit or vegetables, exercising, or drinking water), interpersonal relationships (making more contact with others, being more than patient or open-minded, or having more attention for others), personal finance (saving money), or environmental-friendly behavior (recycling). Over the course of three months, we then measured their goal-congruent behavior functioning, self-control capacity, and addiction force to examine how cocky-command related to beliefs performance and addiction strength over fourth dimension.

Methods

Participants and Design

A customs sample was recruited via the population register of the urban center of Utrecht in the Netherlands as well equally social media and the alumni register of Utrecht Academy. Anyone between the age of xviii and 65 who possessed a smartphone was eligible (we could provide a limited number of participants with a smartphone for the duration of the report if they did non possess one, Due north = 5). All participants indicated they wanted to form a habit in the wellness, sustainability, interpersonal, or financial domain.

The within-subjects design consisted of a pre-measurement administered in groups of 2–thirteen participants at a university location,¹ followed by a three-month interval of daily measures administered through an in-firm adult mobile awarding, and after 90–110 days, a post-measurement (again in group sessions at a academy location). In total, 180 people participated in the pre-measurement, of whom 90 participated in the mail service-measurement. Participants took part in the daily measures over a range of 17–110 days (K = 77.0, SD = 26.7). During this time period, the number of bi-weekly cocky-control assessments ranged from ane to 10 (M = 6.5, SD = 2.three), which were alternated with bi-weekly habit strength assessments, of which the number ranged from 2 to 9 (G = v.seven, SD = two.0). In total, 146 participants (118 women; M _{historic period} = 31.ix; SD_age = 12.vii; range 18–61 years) who completed at least one follow-up assessment of habit force were included in the analyses. More than half of them (65.viii%) were community residents (including alumni) and the residuum (34.two%) were bachelor students. Based on participants' postal code (which is indicative of didactics, income, and work status; Netherlands Plant for Social Research), we inferred their socio-economic condition. About 10.iii% of the participants lived in underprivileged neighborhoods, 61.0% lived in middle class neighborhoods, and 26.0% came from privileged neighborhoods (postal code data was missing for 4 participants). Participants' initial level of addiction strength was moderate (M = 3.ane, SD = 1.1).

Process and Materials

Registration

Those who were interested in participating received an information alphabetic character via email, containing a link to register for the study with a unique participation code. In the registration form, participants were reminded of the terms and weather condition (i.due east., voluntary nature of participation, ability to withdraw without caption, etc.), after which they were required to give their consent for participating in the study. Participants could so schedule an appointment for the pre-measurement.

Pre-measurement

Participants came to the university for a pre-measurement as role of a larger longitudinal prospective study on trait self-control (i.eastward., to run across whether cocky-command could exist trained by daily performance of a behavior that requires self-control – which indeed seemed to be the case; de Ridder et al., 2019). As such, the unlike measurements (pre-, app-, and post-) also included measures that were not of interest for the current written report.²

Goal setting

At the kickoff of the study, participants selected a specific behavior they wanted to turn into a habit over the course of the written report. Choices covered wellness, interpersonal, fiscal, and ecological behaviors (e.g., eating fruit, being patient, saving money, recycling). Depending on the type of behavior called, participants could then choose from three to seven contexts for behavioral practice (e.g., eating fruit when having breakfast, existence patient when talking to someone,³ saving money when in the supermarket, or recycling when tidying up). As such, participants could cull which habit they wanted to grade based on 60 preset combinations of behaviors and contexts. See Effigy 1 for an overview of which behaviors were selected by the participants. Information technology was emphasized that the selected behavior needed to be personally relevant to them, had to be a behavior they did not regularly perform notwithstanding, and had to be feasible for them to perform on a daily basis. After selecting a beliefs and context, participants had to specify for themselves what this behavior entailed (e.g., when they chose exercise equally their goal, it was explained that a ten minute routine at home was more than feasible on a daily basis than an hour at the gym). Every bit such, participants were intrinsically motivated and there was room for forming a new habit.

Figure one. Overview of the number of participants selecting each behavior. Please note that exercise ("sporten" in Dutch) and physical action ("bewegen" in Dutch) refers to different types of behaviors. Whereas practice is typically associated with certain rules and competitiveness, but well-nigh of all with high intensity (due east.grand., playing football game, cross fit, running), physical activity refers to more casual and less intense behaviors (e.g., walking or biking, gardening, household chores).

App instructions

For the purpose of this study, we developed a mobile app (which ran on iOS and android) to assess self-control capacity and habit strength on a regular basis. At the end of the pre-measurement, participants were instructed to install and utilize this app for daily tests and questionnaires. Participants were also informed that they would receive a reminder every morn via the mobile app.

App Measurements

Habit forcefulness

Habit strength was assessed bi-weekly with the Self-Study Habit Index (Verplanken and Orbell, 2003), which consists of 12 statements (e.g., '[self-chosen behavior (e.g., eating fruit)] is something I do …frequently; …automatically; …without thinking)'. For each statement, participants indicated to what extent they felt the statement practical to them on a scale from 1 (completely disagree) to vii (completely agree). The calibration proved reliable with a Cronbach's blastoff of.94.^iv

Goal-congruent beliefs performance

On a daily footing, participants indicated (dichotomously) whether or not they had performed the self-called beliefs that 24-hour interval, and whether they performed this behavior in their self-chosen context.⁵

Self-control capacity

Self-control capacity was assessed bi-weekly with the Brief Self-Control Calibration (Tangney et al., 2004), which consists of 13 statements (e.g., "I am adept at resisting temptation" or "People would say I have atomic number 26 self-bailiwick"). For each statement, participants indicated to what extent they felt the statement practical to them on a scale from i (not at all) to 5 (very much). The scale proved reliable with a Cronbach'southward alpha of 0.79.

Data Analysis

Addiction Formation Over Time – Individual Level Analysis

Kickoff, following Lally et al. (2010) approach, nosotros attempted to fit an asymptotic bend to individual participants' habit force scores over fourth dimension (past means of a Least Squares Curve Fit algorithm in Matlab), to then see whether we could predict the individual (rate of) change in habit forcefulness as a function of goal-congruent behavior operation and self-command capacity. However, the individual patterns fluctuated too much (perchance because bi-weekly measurements were too infrequent; M = 5.73, SD = 1.99, range = ii–nine observations per participant; see Figure 2 for the number of observations plotted against the number of participants^vi), and curve fitting could but exist achieved for four.xi% of our participants (see results under point 2, Supplementary Fabric). As an alternative, we likewise tried fitting a less constrained ability curve (y = ax^b), with even less success (2.four%). We therefore decided to analyze the data on the group level instead.

Effigy 2. Number of observations for habit strength (total Northward = 836) plotted against the number of participants (N = 146).

Habit Formation Over Time – Grouping Level Analysis

We examined the data in SPSS 24 with the Linear Mixed Models, using Maximum Likelihood estimation. In the outset analysis, nosotros carried out a growth curve modeling for habit formation, in which a random intercept, and fixed effects of a linear and a quadratic time trend were estimated. In improver, the random slopes of the linear and quadratic tendency were tested to allow for individual differences in the growth curve. In a 2nd assay we tested whether addiction germination was influenced by self-command capacity and the operation of the beliefs. In Model 1, the random intercept was included to determine the intraclass correlation (ICC) of habit force as an indicator of the variance at person level. In Model two, lagged habit strength (i.e., addiction strength at the previous measurement) was entered to analyze addiction formation. Because we controlled for lagged habit strength, the linear and quadratic tendency were not included in this assay. In Model 3, cocky-control chapters at the previous bi-weekly measurement of self-control and daily practise of the chosen behavior (measured past the proportion of daily app-measurements in which the chosen beliefs was performed during the interval between the previous and the current addiction assessment) was entered, every bit well every bit a number of control variables, i.e., the measurement number of bi-weekly habit cess, the length of the interval since the previous habit assessments, and the number of daily behavioral assessments.

Results

Habit Formation Over Time

We first examined whether habit strength increased over time. Figure iii shows a pregnant increase of about 0.8 SD (a large event size according to Cohen, 1992) in habit strength over a period of 110 days with a stronger increase in start of the written report period, leveling off at the end. Both the linear trend (t = xv.30, p < 0.001) and the quadratic trend (t = −three.39, p < 0.001) were significant. Adding the random slopes for the linear (Wald Z = 5.37, p < 0.001) and quadratic (Wald Z = 2.40, p < 0.05) improved the fit of the model, showing that habit formation differed over participants.

Effigy 3. Addiction strength fitted equally a office time, with 95% confidence bands.

Effects of Goal-Congruent Beliefs Operation and Self-Control Capacity on Addiction Formation

Table one shows the results of a hierarchical multilevel analysis of habit formation. Every bit tin exist seen in Model two, habit strength is rather stable and strongly predicted by lagged habit strength at the previous measurement of habit. Nevertheless, inbound lagged self-control capacity and goal-congruent behavior performance in the time period during both habit strength measurements further increased the fit of the model. Cocky-control capacity did non contribute to college habit forcefulness^vii. However, participants who carried out the cocky-chosen behavior more consistently (higher proportion of goal-congruent behavior performance⁸), showed stronger increases in habit strength. In line with the tendency in habit formation shown before, the time of measurement (i.eastward., the umpteenth fourth dimension) had a minor negative influence on habit strength increase. This is in line with the lower increase in habit strength later on during the study period.

Table 1. The multilevel regression of habit strength.

Discussion

People ofttimes struggle in the pursuit of their long-term goals. Equally adept habits may help people in this pursuit, nosotros ready out to proceeds more insight in how good habits are formed in daily life. Nosotros specifically focused on goal-congruent behavior operation and self-control capacity as potential facilitators of habit germination. We were able to examination our hypotheses in a diverse and highly committed sample. Results showed a big increase in habit strength over the class of iii months, which was strongest for participants who consistently performed the self-chosen goal-coinciding beliefs during this time. Reverse to our expectations and previous findings by Galla and Duckworth (2015), however, we did non notice support for self-control capacity equally a predictor of the habit germination process.

One reason why self-control capacity may not have facilitated addiction formation, could be that participants experienced trivial disharmonize betwixt their long-term goal and an immediately gratifying alternative. In contrast to well-controlled lab experiments where participants are simultaneously confronted with goal-congruent stimuli (eastward.g., broccoli) and conflicting temptations (e.g., apple pie), such temptations may not always be present when the opportunity presents itself to perform goal-congruent beliefs in real life. If and then, the reason that participants did not yet regularly perform the desired behavior earlier participating in the study, may non have been considering they were unable to control their behavior in the presence of temptations. Alternatively, in the absenteeism of temptation, participants may accept had difficulty monitoring their behavior and identifying opportunities for goal pursuit. In the current written report monitoring was facilitated past specifying a specific context for goal pursuit and registering their behavior daily via the smartphone application, which may have facilitated goal-coinciding behavior performance, and hence, habit formation. Indeed, monitoring has been proven to be very effective in goal progress and attainment (see Harkin et al., 2016 for meta-analyses; Michie et al., 2009). Futurity enquiry could extend the current findings by assessing how often people run into temptations during long-term goal pursuit and whether its impact on the habit formation process is modulated past self-command chapters. Besides, future research could investigate whether habit formation tin can exist facilitated even more by frequent monitoring at regular intervals during the day.

Another reason why cocky-control may not accept affected habit formation, is because our instructions to participants may have created an clan between the specific, self-chosen behavior and a specific context. Research has shown that if people form specific "if…, then…" plans (also referred to as implementation intentions), in which a specific behavior is linked to a specific context (e.one thousand., if I open the fridge, and so I volition grab the cherry tomatoes), this volition automatically trigger the specific behavior upon run into of the specific context (Gollwitzer, 1999; Webb and Sheeran, 2007). Every bit such, habit strength – or rather, behavioral automaticity – should increase instantly and cocky-control is no longer required. Although nosotros did not inquire our participants to form implementation intentions, our request to select a specific context in which to perform the specific self-chosen behavior may take resulted in cue-behavior associations that facilitate effortless behavior functioning. However, our data likewise as the data of Lally and colleagues (Lally et al., 2010; in which implementation intentions were actually formed) do not seem to support this line of reasoning. Even if cue-behavior associations were formed, they did not result in instant increases in habit forcefulness, as addiction formation unfolded gradually over the course of several months, leaving room for self-command capacity to influence the habit formation process. Information technology would be interesting, though, to further investigate the function of cocky-control chapters in the presence versus absence of cue-behavior associations in an experimental field study.

Yet some other reason for non finding an event of self-control on the habit germination process may be that we focused on trait rather than state self-control. Although trait self-control did increase over time (see de Ridder et al., 2019; and hence, may take benefited the habit formation process), trait self-control is a relatively stable factor. Futurity research should appraise within-individual fluctuations of state cocky-command in the habit formation procedure – preferably also plumbing equipment habit formation on the private level. Our findings propose that more data points are required for such analyses.

In line with previous inquiry (Lally et al., 2010; Fournier et al., 2017), the current (aggregated) information provided support for the asymptotic contribution of repeated goal-congruent behavior operation to the formation of addiction. Unfortunately, we were unable to prove this tendency on the individual level, due to the bi-weekly assessment of habit strength. Hence, future studies would do good from more frequent assessments. These studies may also want to examination further moderators of habit formation, e.g., what type of contextual cues may be the best triggers for behavior, the role of motivation, and how the germination of good habits affect the bad habits they aim to substitute (see besides Gardner and Lally, 2018).

Beside the strengths of our report (a diverse and highly committed sample), information technology is important to note that the cocky-study measurement of habit strength may have been subject field to biases. Although the SRHI is usually used and well validated (Verplanken and Orbell, 2003; Gardner et al., 2011), it would be even more compelling if the electric current findings could be corroborated past more than implicit measures of habit strength, such equally a lexical conclusion task (Meyer et al., 1972). In the current study, we have attempted to measure habit strength past means of a lexical conclusion task in the mobile app. Even so, the mobile app measurements were not sensitive enough to detect any effects (see point 5, Supplementary Cloth). Hereafter research may instead opt for online computer measurements.

To conclude, our written report was the first to track the role of cocky-control capacity in the habit formation procedure in a longitudinal field experiment. Although we did non find evidence for cocky-control as a facilitator of habit formation, the current findings do offer new directions for future research on self-control and other potential moderators in the formation of skillful habits.

Data Availability Statement

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The studies involving man participants were reviewed and approved by The Faculty Ideals Review Board – Faculty of Social and Behavioral Sciences at Utrecht Academy. The patients/participants provided their written informed consent to participate in this study.

Writer Contributions

AW, JB, MG, and DR developed the theory and study design. AW carried out the experiment and information preparations, and took the atomic number 82 in writing of the manuscript. AW and JB performed the individual-level analyses. JY performed the group-level analyses. All authors provided disquisitional feedback and helped to shape the analyses and manuscript.

Disharmonize of Interest

The authors declare that the enquiry was conducted in the absence of whatever commercial or fiscal relationships that could be construed every bit a potential conflict of interest.

Acknowledgments

We would like to give thanks Django den Boer and Roy van Koten for developing the Habit Tracker app, and Demi Blom for recruiting participants and keeping them involved.

Supplementary Material

The Supplementary Material for this commodity can be establish online at: https://world wide web.frontiersin.org/manufactures/10.3389/fpsyg.2020.00560/full#supplementary-cloth

Footnotes

1. ^ The group administration served to allow more participants to commencement around the same time, i.e., to minimize seasonal influences (e.g., new year'south resolutions). We minimized the degree to which participants influenced each other by stressing the importance of independent answers and reactions, besides as the importance of being silent during the measurements. As well, one or ii researchers were e'er nowadays to monitor participants and answer questions.
2. ^ In the pre-measurement we measured explicit habit strength (with the Self-Report Habit Index; Verplanken and Orbell, 2003) and implicit habit strength (by means of a Lexical Determination Task), implicit state self-control (an adjusted version of the mouse-tracker task; Freeman and Ambady, 2010) and explicit self-control chapters (Cursory Self-Control Scale; Tangney et al., 2004), general attributional way (General Attributional Manner Questionnaire; Peterson et al., 1982), goal importance, and motivation. In the smartphone app, behavioral functioning, context meet, and attributions of failure were measured daily, while habit germination, cocky-control capacity, general self-efficacy (General Self-Efficacy Scale; Jerusalem and Schwarzer, 1979), and willpower beliefs (Job et al., 2010) were measured bi-weekly. Additionally, a mouse tracker chore was alternated with a lexical decision task every other day to measure implicit cocky-command and implicit habit formation, respectively. During the post-measurement, participants completed the same tasks and questionnaires as during the pre-measurement, except that the General Attributional Style Questionnaire was replaced by an ego-depletion task.
3. ^ The selection "when talking to someone" could exist further specified into "when talking to a friend/partner/parent/child/neighbor".
4. ^ Nosotros take also run the analyses with the SRBAI subscale, which led to the aforementioned results (run into point 1, Supplementary Textile).
5. ^ For our main assay, we looked at whether the behavior was performed or non, regardless of the context it was performed in. Analyzing whether the behavior was performed in context or non yielded like results.
6. ^ Participants for whom an asymptotic curve could be fitted did non differ from participants for whom an asymptotic bend could not be fitted in their number of data points [M = 6.33, SD = ii.25 vs. Yard = 6.half dozen, SD = 1.42, respectively; F(one,116) = 0.19, p = 0.67] or behavioral consistency [Thou = 0.88, SD = 0.15 vs. Chiliad = 0.77, SD = 0.26, respectively; F(1,144) = one.02, p = 0.31].
7. ^ One might fence that the result of self-control capacity on habit formation is via goal-coinciding behavior performance. However, our data exercise not provide back up for a relationship between lagged cocky-control and goal-congruent behavior functioning (run into de Ridder et al., 2019). Also, entering lagged cocky-control in the model kickoff (in Model 3), and subsequently adding the other variables (in Model 4), does not reveal any human relationship between self-control and habit strength (see point 4, Supplementary Material).
8. ^ Behavioral consistency differed betwixt the different behaviors chosen [F(ix,136) = iii.02, p = 0.003, ηⁱⁱ = 0.17], such that people were about consistent in performing prosocial behaviors, whereas people were least consistent in exercising and saving coin (meet bespeak iii, Supplementary Fabric). Adding the chosen behavior to the model did not improve the model fit [Δχ² (df = 9) = 7.x ns], nor did it change whatever of the reported effects.

References

Adriaanse, M. A., Kroese, F. M., Gillebaart, M., and De Ridder, D. T. D. (2014). Effortless inhibition: habit mediates the relation between self-control and unhealthy snack consumption. Front. Psychol. 5:444. doi: 10.3389/fpsyg.2014.00444

PubMed Abstruse | CrossRef Full Text | Google Scholar

Baumeister, R. F., Vohs, Chiliad. D., and Tice, D. M. (2007). The strength model of self-control. Curr. Direct. Psychol. Sci. 16, 351–355. doi: 10.1111/j.1467-8721.2007.00534.x

CrossRef Total Text | Google Scholar

de Ridder, D., van der Weiden, A., Gillebaart, M., Benjamins, J., and Ybema, J. F. (2019). Just do it: engaging in self-control on a daily basis improves the capacity for cocky-control. Motiv. Sci. doi: x.1037/mot0000158

CrossRef Total Text | Google Scholar

Fournier, 1000., d'Arripe-Longueville, F., Rovere, C., Easthope, C. S., Schwabe, L., El Methni, J., et al. (2017). Effects of circadian cortisol on the evolution of a health habit. Health Psychol. 36, 1059–1064. doi: x.1037/hea0000510

PubMed Abstract | CrossRef Full Text | Google Scholar

Freeman, J. B., and Ambady, North. (2010). MouseTracker: software for studying existent-time mental processing using a figurer mouse-tracking method. Behav. Res. Methods 42, 226–241. doi: x.3758/BRM.42.1.226

PubMed Abstruse | CrossRef Full Text | Google Scholar

Galla, B. M., and Duckworth, A. Fifty. (2015). More than resisting temptation: benign habits mediate the relationship between cocky-control and positive life outcomes. J. Pers. Soc. Psychol. 109, 508–525. doi: 10.1037/pspp0000026

PubMed Abstruse | CrossRef Total Text | Google Scholar

Gardner, B., de Bruijn, Grand.-J., and Lally, P. (2011). A systematic review and meta-analysis of applications of the self-study habit index to nutrition and physical activity behaviours. Ann. Behav. Med. 42, 174–187. doi: 10.1007/s12160-011-9282-0

PubMed Abstract | CrossRef Total Text | Google Scholar

Gardner, B., and Lally, P. (2018). Modelling Addiction Formation and Its Determinants. In The Psychology of Addiction (Cham: Springer International Publishing), 207–229.

Google Scholar

Gillebaart, M., and Adriaanse, M. A. (2017). Self-control predicts exercise beliefs by force of habit, a conceptual replication of Adriaanse et al. (2014). Front end. Psychol. 8:190. doi: 10.3389/fpsyg.2017.00190

PubMed Abstract | CrossRef Full Text | Google Scholar

Gillebaart, M., and de Ridder, D. T. D. (2015). Effortless cocky-control: a novel perspective on response conflict strategies in trait self-command. Soc. Pers. Psychol. Compass 9, 88–99. doi: 10.1111/spc3.12160

CrossRef Full Text | Google Scholar

Gollwitzer, P. M. (1999). Implementation intentions: potent effects of elementary plans. Am. Psychol. 54, 493–503. doi: 10.1037/0003-066X.54.vii.493

CrossRef Full Text | Google Scholar

Harkin, B., Webb, T. 50., Chang, B. P. I., Prestwich, A., Conner, M., Kellar, I., et al. (2016). Does monitoring goal progress promote goal attainment? A meta-analysis of the experimental bear witness. Psychol. Bull. 142, 198–229. doi: 10.1037/bul0000025

PubMed Abstract | CrossRef Full Text | Google Scholar

Hofmann, W., van Koningsbruggen, K. Chiliad., Stroebe, W., Ramanathan, Southward., and Aarts, H. (2010). As pleasure unfolds. Hedonic responses to tempting food. Psychol. Sci. 21, 1863–1870. doi: ten.1177/0956797610389186

PubMed Abstract | CrossRef Full Text | Google Scholar

Jerusalem, M., and Schwarzer, R. (1979). The general self-efficacy calibration.

Google Scholar

Task, V., Dweck, C. Southward., and Walton, G. K. (2010). Ego depletion–is information technology all in your head? implicit theories most willpower affect self-regulation. Psychol. Sci. 21, 1686–1693. doi: 10.1177/0956797610384745

PubMed Abstruse | CrossRef Full Text | Google Scholar

Lally, P., van Jaarsveld, C. H. M., Potts, H. W. Due west., and Wardle, J. (2010). How are habits formed: modelling habit formation in the existent world. Eur. J. Soc. Psychol. 40, 998–1009. doi: x.1002/ejsp.674

CrossRef Full Text | Google Scholar

Meyer, D. East., Schvaneveldt, R. W., and Cerise, M. G. (1972). "Activation of lexical memory," in Proceedings of the meeting in the Psychonomic Society, St. Louis.

Google Scholar

Michie, Due south., Abraham, C., Whittington, C., McAteer, J., and Gupta, S. (2009). Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychol. 28, 690–701. doi: 10.1037/a0016136

PubMed Abstract | CrossRef Total Text | Google Scholar

Muraven, M., and Slessareva, Due east. (2003). Mechanisms of cocky-control failure: motivation and limited resource. Pers. Soc. Psychol. Bull. 29, 894–906. doi: ten.1177/0146167203029007008

PubMed Abstract | CrossRef Full Text | Google Scholar

Neal, D. T., Woods, W., and Drolet, A. (2013). How practise people adhere to goals when willpower is low? The profits (and pitfalls) of strong habits. J. Pers. Soc. Psychol. 104, 959–975. doi: ten.1037/a0032626

PubMed Abstruse | CrossRef Full Text | Google Scholar

Neal, D. T., Wood, Due west., Labrecque, J. S., and Lally, P. (2012). How practise habits guide behavior? Perceived and actual triggers of habits in daily life. J. Exp. Soc. Psychol. 48, 492–498. doi: 10.1016/j.jesp.2011.x.011

CrossRef Full Text | Google Scholar

Ouellette, J., and Wood, West. (1998). Habit and intention in everyday life: the multiple processes by which by behavior predicts future behavior. Psychol. Bull. 124, 54–74. doi: 10.1037/0033-2909.124.i.54

CrossRef Full Text | Google Scholar

Peterson, C., Semmel, A., von Baeyer, C., Abramson, L. Y., Metalsky, G. I., and Seligman, Thousand. E. P. (1982). The attributional style questionnaire. Cogn. Ther. Res. half dozen, 287–299. doi: 10.1007/BF01173577

CrossRef Total Text | Google Scholar

Tangney, J. P., Baumeister, R. F., and Boone, A. L. (2004). Loftier self-control predicts good adjustment, less pathology, better grades, and interpersonal success. J. Pers. 72, 271–324. doi: 10.1111/j.0022-3506.2004.00263.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Verplanken, B., and Orbell, S. (2003). Reflections on past behavior: a cocky-report alphabetize of habit strength1. J. Appl. Soc. Psychol. 33, 1313–1330. doi: 10.1111/j.1559-1816.2003.tb01951.10

CrossRef Full Text | Google Scholar

Webb, T. L., and Sheeran, P. (2006). Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental show. Psychol. Bull. 132, 249–268. doi: 10.1037/0033-2909.132.2.249

PubMed Abstruse | CrossRef Full Text | Google Scholar

Webb, T. Fifty., and Sheeran, P. (2007). How do implementation intentions promote goal attainment? A test of component processes. J. Exp. Soc. Psychol. 43, 295–302. doi: 10.1016/J.JESP.2006.02.001

CrossRef Full Text | Google Scholar

pickworthsheing.blogspot.com

Source: https://www.frontiersin.org/articles/10.3389/fpsyg.2020.00560/full

Share this post